Traditional search engine results typically involve textual summaries of corresponding documents found in the search, along with meta-data such as the title and URL. Accordingly, this information is predominantly textual. Occasionally an image may be shown for specialized documents, such as for animals, celebrities, or videos; however, images represent a small percentage of the overall results. Subsequently, a major hindrance for users is the need to scan the textual results linearly to find documents of interest.
FIG. 1a illustrates how conventional search engine might present results for a relatively abstract query such as “community service” (e.g., array 1). The conventional web search results include a Wikipedia article, a definition provided by internet sources, high-level overviews, and information about organizations (e.g., objects 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50). In this conventional format, each result includes a document title, an excerpt from the document, and a document URL. To review the conventional results, a user typically skims the text from top to bottom of a results page.
Conventional search results focus on summarization using text excerpts. Therefore, the users read significant amount of text while reviewing results, which is often difficult for many users to process easily. Additionally, it will be difficult for many users to manipulate the textual results (e.g., to rearrange the order or adjust the size of individual results) without exporting the data to an external program.
The system and method described here overcome the shortcomings set forth above or other deficiencies in the existing technology.